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                    <h1 class="description center-align post-title">Huggingface 模型初尝试 | 启动 ChatGLM 模型</h1>
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                <h2 id="前言">前言</h2>
<ul>
<li>本系列文章旨在使用 ChatGLM 预处理模型接入业务系统，完成项目助手类插件服务开发调用</li>
<li>基本思路是学会使用基础模型、学会微调基础模型、了解并掌握向量数据库的使用(实时数据交互)、完成业务接入 <u>NLP</u> 服务</li>
<li>本文是系列文章第一篇：启动 ChatGLM 模型</li>
</ul>
<h2 id="提前安装">提前安装</h2>
<h3 id="conda">conda</h3>
<ul>
<li>
<p><a target="_blank" rel="noopener" href="https://docs.conda.io/en/latest/miniconda.html#windows-installers">conda</a> 是python包管理工具、可以安装一个内置的 <a target="_blank" rel="noopener" href="https://www.python.org/">python</a></p>
</li>
<li>
<p><a target="_blank" rel="noopener" href="https://developer.aliyun.com/mirror/anaconda">配置阿里数据源</a></p>
</li>
</ul>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash">conda config <span class="token parameter variable">--set</span> show_channel_urls <span class="token function">yes</span> <span class="token comment"># windows 生成 .condarc 文件配置</span>
conda clean <span class="token parameter variable">-i</span> <span class="token comment"># 配置完成后，清理索引缓存;JSONDecodeError 异常可以尝试执行该命令</span>
conda config <span class="token parameter variable">--show</span> channels <span class="token comment"># 展示所有镜像源</span><span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span></span></code></pre>
<ul>
<li>用户目录下 .condarc 文件配置</li>
</ul>
<pre class="line-numbers language-text" data-language="text"><code class="language-text">channels:
  - defaults
show_channel_urls: true
default_channels:
  - http://mirrors.aliyun.com/anaconda/pkgs/main
  - http://mirrors.aliyun.com/anaconda/pkgs/r
  - http://mirrors.aliyun.com/anaconda/pkgs/msys2
custom_channels:
  conda-forge: http://mirrors.aliyun.com/anaconda/cloud
  msys2: http://mirrors.aliyun.com/anaconda/cloud
  bioconda: http://mirrors.aliyun.com/anaconda/cloud
  menpo: http://mirrors.aliyun.com/anaconda/cloud
  pytorch: http://mirrors.aliyun.com/anaconda/cloud
  simpleitk: http://mirrors.aliyun.com/anaconda/cloud<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<ul>
<li>检查是否安装成功</li>
</ul>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash">conda <span class="token parameter variable">--version</span><span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>
<ul>
<li>创建一个新的项目环境；后续所有安装都在该环境下进行</li>
</ul>
<pre class="line-numbers language-shel" data-language="shel"><code class="language-shel">conda create -n chatglm python=3.10 # 创建一个新的环境，需要指定python版本
conda env list # 展示所有环境 conda env remove --name chatglm
conda activate chatglm # 激活新创建的环境; conda deactivate # 退出当前环境<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span></span></code></pre>
<h3 id="pytorch">pytorch</h3>
<ul>
<li><a target="_blank" rel="noopener" href="https://pytorch.org">pytorch</a> # 根据 CUDA 版本选择正确的安装命令; <code>nvidia-smi</code> 查看对应 version</li>
</ul>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash">nvidia-smi <span class="token comment"># 查询显存大小，至少需要4.3G显存(window默认使用 GPU专用内存)</span>
<span class="token comment"># 当前系统查询出来 CUDA版本为 11.6；前往历史版本下载对应的环境版本</span>
conda <span class="token function">install</span> <span class="token assign-left variable">pytorch</span><span class="token operator">==</span><span class="token number">1.13</span>.1 <span class="token assign-left variable">torchvision</span><span class="token operator">==</span><span class="token number">0.14</span>.1 <span class="token assign-left variable">torchaudio</span><span class="token operator">==</span><span class="token number">0.13</span>.1 pytorch-cuda<span class="token operator">=</span><span class="token number">11.6</span> <span class="token parameter variable">-c</span> pytorch <span class="token parameter variable">-c</span> nvidia
<span class="token comment"># 当conda下载速度过慢，使用pip直接下载</span>
pip <span class="token function">install</span> <span class="token assign-left variable">torch</span><span class="token operator">==</span><span class="token number">1.13</span>.1+cu116 <span class="token assign-left variable">torchvision</span><span class="token operator">==</span><span class="token number">0.14</span>.1+cu116 <span class="token assign-left variable">torchaudio</span><span class="token operator">==</span><span class="token number">0.13</span>.1 --extra-index-url https://download.pytorch.org/whl/cu116
<span class="token comment"># Err:  HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Read timed out</span>
pip3 config <span class="token parameter variable">--global</span> <span class="token builtin class-name">set</span> global.index-url https://mirrors.aliyun.com/pypi/simple/<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<h3 id="MinGW">MinGW</h3>
<ul>
<li>
<p>安装 <a target="_blank" rel="noopener" href="https://www.mingw-w64.org/downloads/#w64devkit">MinGW</a></p>
</li>
<li>
<p>推荐使用 <a target="_blank" rel="noopener" href="https://jmeubank.github.io/tdm-gcc/">TDM-GCC</a> 安装</p>
</li>
<li>
<p>检查是否安装成功</p>
</li>
</ul>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash">gcc <span class="token parameter variable">-v</span><span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>
<h2 id="下载并启动-chatGLM-代码">下载并启动 chatGLM 代码</h2>
<h3 id="下载仓库代码">下载仓库代码</h3>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash"><span class="token function">git</span> clone git@github.com:THUDM/ChatGLM-6B.git <span class="token comment"># https://github.com/THUDM/ChatGLM-6B.git</span><span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>
<h3 id="下载预处理模型">下载预处理模型</h3>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash"><span class="token comment"># git submodule add git@hf.co:THUDM/chatglm-6b-int4 ./THUDM/chatglm-6b-int4</span>
<span class="token comment"># 在项目根目录下创建`THUDM`模型文件夹，同时进入目录克隆模型[注册一个账户配置 ssh 可以下载更快]</span>
<span class="token function">git</span> clone git@hf.co:THUDM/chatglm-6b-int4 <span class="token comment"># https://huggingface.co/THUDM/chatglm-6b-int4</span>

<span class="token comment"># git大文件存储工具</span>
<span class="token function">git</span> lfs <span class="token function">install</span>
<span class="token builtin class-name">cd</span> chatglm-6b-int4
<span class="token comment"># 大文件下载；如果不能下载，前往下方地址手动下载模型置于当前文件夹</span>
<span class="token function">git</span> lfs pull
<span class="token comment"># 如果出现 `Error updating the git index Unable to process path pytorch_model.binUnable to process path pytorch_model.bin`; 尝试执行下面语句</span>
<span class="token function">git</span> reset <span class="token parameter variable">--hard</span> <span class="token comment"># 执行之后重新拉取代码即可</span><span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<p><a target="_blank" rel="noopener" href="https://cloud.tsinghua.edu.cn/d/674208019e314311ab5c/">清华大学模型下载地址</a></p>
<table>
<thead>
<tr>
<th>模型名称</th>
<th>模型大小</th>
<th>模型所需GPT与内存</th>
</tr>
</thead>
<tbody>
<tr>
<td>chatglm-6b</td>
<td>12.4g</td>
<td>最低13G显存，16G内存</td>
</tr>
<tr>
<td>chatglm-6b-int8</td>
<td>7.2G</td>
<td>最低8G显存</td>
</tr>
<tr>
<td>chatglm-6b-int4</td>
<td>3.6G</td>
<td>最低4.3G显存</td>
</tr>
</tbody>
</table>
<h3 id="运行web示例demo">运行web示例demo</h3>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash">conda <span class="token function">install</span> <span class="token parameter variable">--file</span> requirements.txt <span class="token comment"># 提示找不到包时可增加其他镜像源</span>

<span class="token comment"># 添加清华镜像源</span>
conda config <span class="token parameter variable">--add</span> channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config <span class="token parameter variable">--add</span> channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config <span class="token parameter variable">--add</span> channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
conda config <span class="token parameter variable">--add</span> channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
conda config <span class="token parameter variable">--show</span> channels

<span class="token comment"># pip install -r requirements.txt -i https://mirror.sjtu.edu.cn/pypi/web/simple</span>
pip <span class="token function">install</span> mdtex2html torch cpm_kernels <span class="token comment"># 提示找不到包尝试用pip命令安装</span>
<span class="token comment"># 下载模型文件, 创建`THUDM`文件夹</span>
<span class="token comment"># 需要更改文件上方模型路径; 原为 `THUDM/chatglm-6b` &gt; `THUDM\chatglm-6b-int4`; win环境下文件分隔符不同</span>
python web_demo.py<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<ul>
<li>项目加载模型数据</li>
</ul>
<img src="/chatglm/%E9%A1%B9%E7%9B%AE%E5%8A%A0%E8%BD%BD%E6%A8%A1%E5%9E%8B%E6%95%B0%E6%8D%AE.png" class="" title="项目加载模型数据">
<h2 id="无GPU部署JittorLLMs">无GPU部署<code>JittorLLMs</code></h2>
<h3 id="更新jittor环境">更新<code>jittor</code>环境</h3>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash"><span class="token function">git</span> clone git@github.com:Jittor/JittorLLMs.git
<span class="token builtin class-name">cd</span> JittorLLMs
conda create <span class="token parameter variable">-n</span> jittor-chatglm <span class="token assign-left variable">python</span><span class="token operator">=</span><span class="token number">3.10</span> <span class="token comment"># 新建一个环境尝试简单显存要求</span>
conda activate jittor-chatglm
<span class="token comment"># -i 指定用jittor的源， -I 强制重装Jittor版torch</span>
pip <span class="token function">install</span> <span class="token parameter variable">-r</span> requirements.txt <span class="token parameter variable">-i</span> https://pypi.jittor.org/simple <span class="token parameter variable">-I</span><span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<ul>
<li>requirements.txt 依赖声明如下</li>
</ul>
<pre class="line-numbers language-text" data-language="text"><code class="language-text"># requirements.txt
jittor == 1.3.8.5
jtorch == 0.1.7
torch
torchvision<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<h2 id="部署Langchain-Chatchat"><s>部署<code>Langchain-Chatchat</code></s></h2>
<blockquote>
<p><code>Langchain-Chatchat</code> 一个基于 <code>chatGLM</code> 的本地知识库实现</p>
</blockquote>
<h3 id="下载代码">下载代码</h3>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash"><span class="token function">git</span> clone git@github.com:chatchat-space/Langchain-Chatchat.git <span class="token comment"># https://github.com/chatchat-space/Langchain-Chatchat</span><span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>
<h3 id="下载模型">下载模型</h3>
<p>可参考上一节使用<code>git lfs pull</code>进行大文件下载</p>
<p>以本项目中默认使用的&nbsp;LLM&nbsp;模型&nbsp;<a target="_blank" rel="noopener" href="https://huggingface.co/THUDM/chatglm2-6b">THUDM/chatglm2-6b</a>&nbsp;与&nbsp;Embedding&nbsp;模型&nbsp;<a target="_blank" rel="noopener" href="https://huggingface.co/moka-ai/m3e-base">moka-ai/m3e-base</a>&nbsp;为例</p>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash"><span class="token function">git</span> clone git@hf.co:THUDM/chatglm-6b-int4 <span class="token comment"># https://huggingface.co/THUDM/chatglm-6b-int4</span>
<span class="token function">git</span> clone git@hf.co:moka-ai/m3e-base <span class="token comment"># https://huggingface.co/moka-ai/m3e-base</span><span aria-hidden="true" class="line-numbers-rows"><span></span><span></span></span></code></pre>
<h3 id="更改配置文件">更改配置文件</h3>
<p>复制项目文件 <code>./configs/model_config.py.example</code> &gt; ``./configs/model_config.py`</p>
<p>复制项目文件 <code>./configs/server_config.py.example</code> &gt; ``./configs/model_config.py`</p>
<h3 id="初始化知识库">初始化知识库</h3>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash">conda create <span class="token parameter variable">-n</span> chatchat <span class="token assign-left variable">python</span><span class="token operator">=</span><span class="token number">3.10</span> <span class="token comment"># 新的conda环境</span>
conda activate chatchat
conda <span class="token function">install</span> <span class="token parameter variable">--file</span> requirements.txt<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span></span></code></pre>
<h2 id="其他代码仓库地址">其他代码仓库地址</h2>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash"><span class="token function">git</span> clone git@github.com:THUDM/ChatGLM2-6B.git <span class="token comment"># https://github.com/THUDM/ChatGLM2-6B # chatGLM 升级内容</span>

<span class="token function">git</span> clone git@github.com:THUDM/CodeGeeX2.git <span class="token comment"># https://github.com/THUDM/CodeGeeX2 # chatGLM 代码能力加强模型</span>

<span class="token function">git</span> clone git@hf.co:TMElyralab/lyraChatGLM.git <span class="token comment"># https://huggingface.co/TMElyralab/lyraChatGLM # 对 ChatGLM-6B 进行推理加速，最高可以实现 9000+ tokens/s 的推理速度</span>

<span class="token function">git</span> clone git@github.com:wangzhaode/ChatGLM-MNN.git <span class="token comment"># https://github.com/wangzhaode/ChatGLM-MNN # 一个基于 MNN 的 ChatGLM-6B C++ 推理实现，支持根据显存大小自动分配计算任务给 GPU 和 CPU</span>

<span class="token function">git</span> clone git@github.com:Jittor/JittorLLMs.git <span class="token comment"># https://github.com/Jittor/JittorLLMs # 最低3G显存或者没有显卡都可运行 ChatGLM-6B FP16， 支持Linux、windows、Mac部署</span>

<span class="token function">git</span> clone git@github.com:MegEngine/InferLLM.git <span class="token comment"># https://github.com/MegEngine/InferLLM # 轻量级 C++ 推理，可以实现本地 x86，Arm 处理器上实时聊天，手机上也同样可以实时运行，运行内存只需要 4G</span><span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<h2 id="名词解释">名词解释</h2>
<h3 id="NLP">NLP</h3>
<p>NLP 是自然语言处理（Natural Language Processing）的缩写，本文学习的 transformers 是一种自然语言处理的思想及技术</p>
<h3 id="Hugging-face">Hugging face</h3>
<p>Hugging face 起初是一家聊天机器人初创服务商，其在github上开源了 Transformers python 库。<br>
还提供了 Trainer （构建和训练自定义模型的框架） 、 Tuning （用于微调预训练模型的库）等库。<br>
这些工具和库都使用 Python 编写，并支持多种不同的深度学习框架，如 PyTorch 和 TensorFlow 等。<br>
主要目的是为 NLP 社区提供一个统一的框架，便于研究人员和开发人员能够轻松地使用最先进的预训练模型，并将其应用于各种不同的 NLP 任务中。</p>
<h3 id="PyTorch">PyTorch</h3>
<p>PyTorch 是一个开源的Python机器学习库，Hugging face很多模型需要前置安装 PyTorch</p>
<h3 id="Transformer">Transformer</h3>
<p>Transformer 是 <u>Hugging face</u> 提供的自然语言处理工具库，也是当前比较流行的机器学习工具库。<br>
因为NLP通常是多个任务顺序而成，通常使用 <code>transformer</code> 中 <code>pipeline</code> 进行流水线工作</p>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash"><span class="token comment"># pip 是 python 包管理工具, Python 3.4+ 自带pip工具</span>
pip <span class="token function">install</span> <span class="token assign-left variable">transformers</span><span class="token operator">==</span><span class="token number">4.26</span>.1 <span class="token comment"># 安装指定版本</span>
pip <span class="token function">install</span> tensorflow
<span class="token comment"># 如果你是conda的话</span>
conda <span class="token function">install</span> <span class="token parameter variable">-c</span> huggingface transformers  <span class="token comment"># 4.0以后的版本才会有</span><span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<h3 id="requirements-txt">requirements.txt</h3>
<p>Python项目中必须包含一个 requirements.txt 文件，用于记录所有依赖包及其精确的版本号。以便新环境部署。</p>
<pre class="line-numbers language-bash" data-language="bash"><code class="language-bash">pip freeze <span class="token operator">&gt;</span> requirements.txt <span class="token comment"># 生成requirements.txt</span>
pip <span class="token function">install</span> <span class="token parameter variable">-r</span> requirements.txt <span class="token comment"># 从requirements.txt安装依赖</span><span aria-hidden="true" class="line-numbers-rows"><span></span><span></span></span></code></pre>
<h2 id="参考资料">参考资料</h2>
<ul>
<li><a target="_blank" rel="noopener" href="https://huggingface.co/models">Hugging face 官方网站</a></li>
<li><a target="_blank" rel="noopener" href="https://zhuanlan.zhihu.com/p/535100411">Huggingface 超详细介绍</a></li>
<li><a target="_blank" rel="noopener" href="https://blog.csdn.net/qq_51116518/article/details/130299417">从零开始的ChatGLM 配置详细教程</a></li>
</ul>

                
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